Skip to content

Updated cholesky_backward for complex inputs #45267

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
IvanYashchuk wants to merge 5 commits into from
Closed

Updated cholesky_backward for complex inputs #45267

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
5 commits
Select commit Hold shift + click to select a range
0611218
Updated cholesky_backward for complex input
IvanYashchuk Sep 24, 2020
44f8c9d
torch.sum is not needed in test_complex_cholesky
IvanYashchuk Sep 24, 2020
3670bc1
flake8
IvanYashchuk Sep 24, 2020
76b75cc
skipIfNoLapack
IvanYashchuk Sep 24, 2020
75051fa
Merged real and complex cholesky tests
IvanYashchuk Sep 24, 2020
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
26 changes: 16 additions & 10 deletions test/test_autograd.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -31,7 +31,7 @@
from torch.utils.checkpoint import checkpoint
from torch.testing._internal.common_utils import (TEST_MKL, TEST_WITH_ROCM, TestCase, run_tests, skipIfNoLapack,
suppress_warnings, slowTest,
load_tests, random_symmetric_pd_matrix, random_symmetric_matrix,
load_tests, random_symmetric_matrix,
IS_WINDOWS, IS_MACOS, CudaMemoryLeakCheck)
from torch.autograd import Variable, Function, detect_anomaly
from torch.autograd.function import InplaceFunction
Expand Down Expand Up @@ -2501,22 +2501,28 @@ def test_var_mean_differentiable(self):
@skipIfNoLapack
def test_cholesky(self):
def func(root, upper):
x = torch.matmul(root, root.transpose(-1, -2)) + 1e-05
x = 0.5 * (root + root.transpose(-1, -2).conj())
return torch.cholesky(x, upper)

def run_test(upper, dims):
root = torch.rand(*dims, requires_grad=True)
def run_test(upper, dims, dtype):
root = torch.rand(*dims, dtype=dtype, requires_grad=True)
root = root + torch.eye(dims[-1])

gradcheck(func, [root, upper])
gradgradcheck(func, [root, upper])
# TODO: gradgradcheck does not work correctly yet for complex
if not dtype.is_complex:
gradgradcheck(func, [root, upper])

root = random_symmetric_pd_matrix(dims[-1], *dims[:-2]).requires_grad_()
root = torch.rand(*dims, dtype=dtype)
root = torch.matmul(root, root.transpose(-1, -2).conj())
root.requires_grad_()
chol = root.cholesky().sum().backward()
self.assertEqual(root.grad, root.grad.transpose(-1, -2)) # Check the gradient is symmetric
self.assertEqual(root.grad, root.grad.transpose(-1, -2).conj()) # Check the gradient is hermitian

for upper, dims in product([True, False], [(3, 3), (4, 3, 2, 2)]):
run_test(upper, dims)
run_test(upper, dims)
for upper, dims, dtype in product([True, False],
[(3, 3), (4, 3, 2, 2)],
[torch.double, torch.cdouble]):
run_test(upper, dims, dtype)

@skipIfNoLapack
def test_cholesky_solve(self):
Expand Down
10 changes: 5 additions & 5 deletions torch/csrc/autograd/FunctionsManual.cpp
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -711,15 +711,15 @@ Tensor cholesky_backward(Tensor grad, bool upper, Tensor L) {
// leads to stable gradient updates, and retains symmetry of the updated matrix if it
// were updated by a gradient based algorithm.
if (upper) {
L = L.transpose(-1, -2);
grad = grad.transpose(-1, -2);
L = L.transpose(-1, -2).conj();
grad = grad.transpose(-1, -2).conj();
}
auto L_inverse = std::get<0>(at::triangular_solve(at::eye(L.size(-1), L.options()), L, /*upper=*/false));
auto phi = at::matmul(L.transpose(-1, -2), grad);
auto phi = at::matmul(L.transpose(-1, -2).conj(), grad);
phi.tril_().diagonal(/*offset=*/0, /*dim1=*/-2, /*dim2=*/-1).mul_(0.5);

auto grad_input = at::matmul(at::matmul(L_inverse.transpose(-1, -2), phi), L_inverse);
return grad_input.add(grad_input.transpose(-1, -2)).mul_(0.5); // Symmetrizing the gradient
auto grad_input = at::matmul(at::matmul(L_inverse.transpose(-1, -2).conj(), phi), L_inverse);
return grad_input.add(grad_input.transpose(-1, -2).conj()).mul_(0.5); // Symmetrizing the gradient
}

Tensor cholesky_inverse_backward(Tensor grad, Tensor L, bool upper, Tensor inverse) {
Expand Down